Vision

vision (router)

Nested swiss-knife reference for image understanding. Three paths — pick the cheapest available.

Decision Tree

Is `vision` tool in your tool list?
├── YES → use it directly, done.
└── NO  → does the user have a MiniMax preset/key slot?
         (read the sibling `../minimax-cli/SKILL.md`; scan ~/.lingtai-tui/presets/** without printing keys)
         ├── YES → use the `minimax-cli` reference for `mmx vision …` from the shell, or an already-registered MCP tool
         └── NO  → fall back to local VLM:
                  python3 <skill-path>/scripts/describe.py <image>
                  See reference/local-models.md.

Path 1 — Built-in vision Tool

If your LLM provider supports image input (MiniMax, Gemini, Anthropic, OpenAI, Zhipu), the kernel exposes a vision tool directly. Cheapest, lowest latency, no extra setup.

vision not in your tool list? Your LLM provider doesn't support image input — fall through to Path 2 or 3.

Path 2 — MiniMax via the sibling minimax-cli reference

For text-only LLMs (DeepSeek, OpenRouter text-only, Codex) with a usable MiniMax preset/key slot. Two routes can exist:

• Shell — mmx vision … via the official CLI. No MCP registration needed; just install + key. Best for ad-hoc one-shots in bash.
• In-tool — an already-registered MiniMax MCP vision tool. Best when the agent needs vision as a tool call inside a longer reasoning loop. MCP server registration is owned by mcp-manual (kernel mcp capability).

Read the sibling minimax-cli reference (../minimax-cli/SKILL.md) before using the shell route. It owns the canonical MiniMax credential discovery flow: scan TUI presets recursively, pick the declared slot, export it without printing the key, and match the preset region/base URL. Do not assume a bare MINIMAX_API_KEY is the only valid slot.

Path 3 — Local VLM (offline, unlimited)

For agents that need image analysis without a vision-capable LLM and without a MiniMax key. Or for batch jobs / privacy-sensitive content / unlimited quota.

Run a Hugging Face vision-language model locally:

python3 <skill-path>/scripts/describe.py <image-path> [--prompt PROMPT] [--model qwen2-vl-2b|moondream2|qwen2-vl-7b]

First call downloads weights (2–15 GB depending on model), subsequent calls reuse the cache. The script auto-installs transformers + torch + model-specific deps via lingtai.venv_resolve.ensure_package.

Output is JSON on stdout: {image, backend, prompt, response, elapsed_seconds}. Errors → stderr, non-zero exit.

See reference/local-models.md for model selection, hardware tradeoffs, prompt templates per model, batch patterns (load model once, loop many images), and failure modes.

When NOT to use this skill

• Your LLM already has vision in its tool list — Path 1, no decision needed.
• You need to generate an image — use the sibling minimax-cli reference (../minimax-cli/SKILL.md, mmx image …).
• You need to describe video frames — extract frames with ffmpeg first, then loop this skill over them.

Found a bug or issue? If you encounter any problems with this skill, load the lingtai-issue-report skill and follow its instructions to report it.